Few innovations have shaped modern medicine like the discovery of X-rays. Wilhelm Conrad Roentgen’s 1895 discovery would completely change the diagnostic process. Today, their use is so widespread and our understanding of them so commonplace that it’s difficult to imagine medicine before the X-ray.

And yet, at the time many were dubious. So much so that the New York Times initially mocked the news, describing it as an “alleged discovery of how to photograph the invisible.”

Today, we hear about medical advances every other day. It can be difficult to separate the legitimate revolutions from the fluff. Is this new medication a miracle pill? Will this new procedure really cure that? Can you rely on this celebrity doctor to give you the straight facts . . . or is he just peddling high-tech snake oil?

It’s hard to tell. In the current environment, news of machine learning and its potential impact on healthcare can sound like overly-hyped science fiction. But machine learning is the real deal.

As Forbes described in a recent article, “machine learning is about teaching computers to learn in the same way we do, by interpreting data from the world around us, classifying it and learning from its successes and failures. In fact, machine learning is a subset, or better, the leading edge of artificial intelligence.” It’s both real and potentially revolutionary for healthcare.

Below are five ways machine learning is changing the medical field.

Diagnosis

In October of last year, IBM Watson Health formed a partnership with Quest Diagnostics to create IBM Watson Genomics. The goal is to change the way tumors are diagnosed using cognitive computing. Machine learning is a key component of cognitive computing, which could be defined as “the simulation of human thought processes in a computerized model.”

This is just one area where machine learning could make an enormous difference in our ability to accurately diagnose disease. If we can harness the power of machine learning to identify diagnosable patterns, the implications for cancer treatment could be tremendous.

Imaging Analytics

Radiology is a uniquely challenging area of medicine. It’s not isolated to the review of static images. Rather, a significant part of a radiologist’s job is to observe the differences between imagines. Sometimes those differences are minute but critical.

Machine learning can help in this capacity. Advanced algorithms can detect extremely subtle changes, supplementing what a human eye might see.

Pharmaceutical R&D

Currently, the primary application of machine learning in pharmaceutical science is in the early stage of drug discovery. The process of identifying new patterns for potential therapeutic approaches can be tackled much more efficiently by a computer than by a human.

We’re nowhere near an unsupervised environment, here. Machine learning is being utilized as a discovery tool with the understanding that advanced R&D will require human oversight. However, the advantages of machine learning for drug development are still compelling and promising.

Smart Medical Records

Machine learning can assist in everything from document classification to the sorting and transfer of records. Additionally, there’s plenty of opportunity to use the new technology for optical character recognition – the process of converting handwritten notes to digital characters.

These tasks can be both tedious and slow for a human. Machine learning boosts efficiency within the medical field without sacrificing precision.

Cybersecurity

Privacy is a major concern within the medical field. Patient records are full of sensitive information. The need for the strongest levels of cybersecurity cannot be overstated, particularly with instances of ransomware on the rise.

Machine learning can be utilized to monitor healthcare networks, proactively seeking out potential threats before they have a chance to compromise patient data. In time, it’s likely machine learning will play a critical role in all cybersecurity.

Moving Into the Future

These kinds of advances are exciting. Machine learning has real potential to contribute to healthcare providers’ ability to deliver higher quality of service.

Here at Process Fusion, we make it a point to stay up-to-date on the latest developments. We specialize in data capture, transfer and storage. We’re fascinated by the impact on the medical field and proud to pass along new information as it becomes available.